Machine Learning Fundamentals

Define machine learning and its significance. Highlight how machines learn from data to automate tasks, improve decision making, and enable personalization across healthcare, finance, retail, transportation, and daily life.

Trace the history and evolution of machine learning from the 1950s to today, covering milestones like the Turing test, the perceptron, and early neural networks.

Explore the three major types of machine learning—supervised, unsupervised, and reinforcement—how they learn from data and solve real-world problems.

Explore key terminologies in machine learning, including datasets, features, labels, training and testing sets, algorithms, models, and evaluation concepts like overfitting, underfitting, cross-validation, and hyperparameters.

Learn how machine learning models are trained on training data, tested on new inputs, and evaluated for accuracy. Observe retraining with tweaks and more data, then deployment for real-world predictions.

Learn supervised learning with labeled data and algorithms like linear and logistic regression, decision trees, SVM, and KNN, model evaluation and applications in fraud detection, medical diagnosis, and autonomous vehicles.

Discover supervised learning, a labeled-data approach that trains models, predicts outcomes, and evaluates accuracy, precision, recall, and f1 score for real-world tasks.

Explore k-nearest neighbors (KNN), a nonparametric, lazy learning method for classification and regression. It predicts outcomes from the K nearest data points using distance metrics.

Preview supervised learning concepts and evaluation metrics, including linear and logistic regression, decision trees, SVM, and KNN; set the stage for the next module on unsupervised learning.

Explore reinforcement learning, where an agent learns from trial and error within an environment to maximize cumulative rewards, guided by policy and value functions across domains like gaming and robotics.

Balance exploration and exploitation to maximize long-term returns in reinforcement learning. Apply strategies like epsilon-greedy and upper-confidence-bound to optimize decisions, with relevance in advertising, A/B testing, and robotics.

Explore reinforcement learning fundamentals—agents, environments, states, actions, rewards, transitions, and policies. See how Q-learning and deep Q-networks enable real-world applications and future directions in NLP and computer vision.

Explore the core techniques of regression and classification, including simple linear regression, multiple linear regression, logistic regression, and genetic algorithms, and learn how to evaluate predictive models across six lessons.

Review the foundations of regression and classification, covering simple and multiple linear regression, logistic regression with sigmoid, odds and logit, decision boundaries, and genetic algorithms for logistics optimization.

Master train-test splitting by randomizing data, using stratified splits and cross-validation to ensure training learns patterns and testing evaluates generalization while preventing data leakage and overfitting.

Explore how precision, recall, and the F1 score measure model effectiveness, guiding decisions in medical diagnosis, spam detection, fraud detection, and search engines.

Explore ROC curves and AUC to evaluate classification models, interpret true positive and false positive rates across thresholds, and tune decisions for robust, well-separated classes.

Consolidate what we've learned across the previous modules and bridge learning to application in module 7, featuring a course summary of essential takeaways and a final exam to validate understanding.